Secrets Of Data Analytics Leaders

The data mesh is an attempt by software engineers to remake the data industry in their image. There is a lot of goodness in the data mesh but will it work? The track record for other reform efforts is not good, although new technologies are putting wind into the methodology. Published at: https://www.eckerson.com/articles/data-mesh-translated-software-engineers-try-to-reform-data

Data Stewardship Experience strategies (personal growth, community, societal contribution, and disruptive innovation) can meet several cognitive, social, and psychological needs, and motivate professionals to become productive data stewards. It also removes the stigma of data governance as a rigid and bureaucratic gatekeeping discipline. Published at: https://www.eckerson.com/articles/improving-the-data-stewardship-experience-dsx-productive-motivational-strategies-for-data-governance

Decision AI is an emerging discipline that enables faster, smarter operational decisions by combining decision intelligence and contextual intelligence. Published at: https://www.eckerson.com/articles/decision-ai-and-the-opportunity-for-smarter-faster-actions

This audio blog, the third and final in a series, recommends five guiding principles for success. Published at: https://www.eckerson.com/articles/the-customer-360-data-program-and-cloud-connectors-guiding-principles-for-success

Consider key trends and challenges as you design an effective organizational architecture for data governance while generating value with pervasive analytics. Published at: https://www.eckerson.com/articles/organizational-architecture-can-make-or-break-your-data-governance-program

At Summit 2022, Snowflake paints a picture of a utopian data future where all data is treated equally regardless of where it comes from or what language is used to process it, where processing resources are limitless, and all data is secured and governed. Published at: https://www.eckerson.com/articles/snowflake-summit-2022-growth-vision-and-strategies

Providing analysts with tools that increase their efficiency is critical to helping them do more work with less effort. Published at: https://www.eckerson.com/articles/a-data-analyst-s-guide-to-the-data-catalog

This blog recommends criteria for enterprises to evaluate cloud connectors and ensure the benefits outweigh the costs. Published at: https://www.eckerson.com/articles/evaluating-cloud-connectors-for-the-customer-360-data-program

Machine learning models help respond to the time-based value and risks of business events. To achieve this on an ongoing basis, enterprises should build a streaming ML program based on sound business objectives, a cross-functional team, open platforms, and phased execution. Published at: https://www.eckerson.com/articles/machine-learning-and-streaming-data-pipelines-part-iii-guiding-principles

Enterprise data teams embrace DataOps to achieve new levels of efficiency and effectiveness in delivering data-driven solutions. Roche shows what’s possible when you combine a state-of-the-art cloud data platform with a data mesh architecture and DataOps solution. Published at: https://www.eckerson.com/articles/what-can-dataops-do-for-you-ask-roche

As we share data, we create data webs. If we allow copies of our data to proliferate throughout these webs, we reduce the value of the data and create data governance challenges. The solution is new, ownership-centric approaches to data sharing that don’t rely on traditional copy-based integration. Published at: https://www.eckerson.com/articles/zero-copy-approaches-to-data-sharing

Treating data assets as products helps businesses increase internal data consumption and become more data driven. It also creates opportunities for monetization. But to succeed in either scenario requires product-market fit. This article presents a process for finding it. Published at: https://www.eckerson.com/articles/7-steps-for-building-a-valuable-data-product

It’s tempting to dismiss observability as another overused buzzword. But this emerging discipline offers substantive methods for enterprises to monitor and optimize business metrics, IT operations, data pipelines, machine learning models, and data quality. Published at: https://www.eckerson.com/articles/the-five-shades-of-observability-business-operations-pipelines-models-and-data-quality

An analytics center of excellence is the cornerstone of every data strategy, yet few data leaders know how to design one that works effectively. The key is to embrace federated techniques that balance standards and speed, agility, and governance. This article explains the core components of an analytics center of excellence. Published at: https://www.eckerson.com/articles/how-to-design-an-analytics-center-of-excellence

Companies are investing in new solutions—such as data fabric, data access governance, and data observability—to keep pace with expanding business appetite for data. Pervasive use of metadata to solve data management problems means that metadata is itself a valuable data asset that we must proactively manage. Published at: https://www.eckerson.com/articles/metadata-is-data-so-manage-it-like-data

Data engineers are often responsible for numerous types of data tests: unit tests, integration/component tests, performance tests, and end-to-end tests. Their best hope is to find the automated data testing tools that work for their technologies and try them. Published at: https://www.eckerson.com/articles/testing-capabilities-and-tools-for-data-engineers-part-2

Data integration, governance, and consumption play a pivotal role in the machine learning lifecycle. New offerings from Informatica illustrate the types of tools data science teams need to handle data integration, governance, and consumption. Published at: https://www.eckerson.com/articles/integrating-governing-and-consuming-data-for-the-machine-learning-lifecycle

Data architecture-as-a-service or DAaaS is a metadata-driven approach that injects SQL guardrails into no-code data development tools so business users can develop their own data pipelines without creating data silos. This article defines 10 criteria for evaluating DAaaS-based products. Published at: https://www.eckerson.com/articles/data-architecture-as-a-service-how-to-evaluate-products

Today’s data architecture discussions are heavily biased toward managing data for analytics, with attention to big data, scalability, cloud, and cross-platform data management. We need to acknowledge analytics bias and address management of operational data. Ignoring operational data architecture is a sure path to technical debt and future data management pain. Published at: https://www.eckerson.com/articles/the-yin-and-yang-of-data-architecture

This audio blog, the first in a series, presents the fundamentals of location intelligence. It will explore how location intelligence has evolved in recent years and the kinds of insights it can provide. Published at: https://www.eckerson.com/articles/location-intelligence-part-i-leveraging-geospatial-data-to-drive-your-organization